Method and computing system for object identification

ABSTRACT

Systems and methods for processing spatial structure data are provided. The system accesses spatial structure data, which describes object structure, and which has depth information indicative of a plurality of layers for the object structure. The system further extracts, from the spatial structure data, a portion of the spatial structure data representative of one layer of the plurality of layers. The system identifies, from the portion of the spatial structure data, a plurality of vertices that describe a contour of the layer. Additionally, the system identifies convex corners of the layer based on the plurality of vertices and performs object recognition according to the convex corners.

FIELD OF THE INVENTION

The present disclosure is related to computing systems and methods forprocessing spatial structure data. In particular, embodiments hereof arerelated to detection of corners of an object whose structure isdescribed in spatial structure data.

BACKGROUND OF THE INVENTION

As automation becomes more common, robots are being used in moreenvironments, such as in warehousing and manufacturing environments. Forinstance, robots may be used to load items onto or off of a pallet in awarehouse, or to pick up objects from a conveyor belt in a factory. Themovement of the robot may be fixed, or may be based on an input, such asspatial structure data obtained by one or more sensors in a warehouse orfactory. Robot guidance may be assisted via object recognition performedaccording to the spatial structure data. Methods and techniques thatimprove object recognition are thus valuable.

SUMMARY

In an embodiment, a computing system including a non-transitorycomputer-readable medium and a processing circuit is provided. Theprocessing circuit is configured, when spatial structure data describingobject structure is stored in the non-transitory computer-readablemedium, to perform the following method: access the spatial structuredata, the spatial structure data having depth information indicative ofa plurality of layers for the object structure; extract, from thespatial structure data, a portion of the spatial structure datarepresentative of one layer of the plurality of layers; identify, fromthe portion of the spatial structure data, a plurality of vertices thatdescribe a contour of the layer. In an embodiment, the non-transitorycomputer-readable medium has instructions that, when executed by theprocessing circuit, causes the processing circuit to perform the methoddescribed above.

BRIEF DESCRIPTION OF THE FIGURES

FIGS. 1A through 1F illustrate a spatial structure sensing device and acomputing system configured for accessing and processing spatialstructure data consistent with embodiments hereof.

FIGS. 2A-2C provide block diagrams that illustrate a computing systemconfigured to process spatial structure data consistent with embodimentshereof.

FIG. 3 provides a flow diagram that illustrates a method of processingspatial structure data, according to an embodiment hereof.

FIGS. 4A-4E illustrate aspects of the operation of a computing systemconsistent with embodiments hereof.

FIGS. 5A-5G illustrate aspects of the operation of a computing systemconsistent with embodiments hereof.

FIGS. 6A-6C illustrate aspects of the operation of a computing systemconsistent with embodiments hereof.

DETAILED DESCRIPTION OF THE FIGURES

The present disclosure provides systems and methods for processingspatial structure data, such as a point cloud, and more specificallyrelates to identifying convex corners from the spatial structure data.In an embodiment, the spatial structure data may describe a structure ofone or more objects (which may be referred to as object structure), andthe convex corners may generally correspond to exterior corners of theobject structure. In some cases, the convex corners may be used toperform object recognition, which may involve determining what object ortype of object is being represented by the spatial structure data. Ifthe spatial structure data is acquired by a spatial structure sensingdevice, such as a depth camera, the object recognition may determinewhat object or type of object is being or has been sensed by the spatialstructure sensing device. In some cases, an output of the objectrecognition may be used by a robot control system to guide movement of arobot or other machinery to interact with the object or objects beingsensed by the spatial structure sensing device. For instance, the robotmay be configured for grasping, lifting, and/or moving objects in awarehouse, factory, or some other environment of the robot. Guidingmovement of the robot may involve adapting the robot's movement todifferent objects or types of object, which may have different shapes,sizes, and/or orientations. More specifically, implementing suchguidance may involve performing object recognition to recognize whatobject or type of object the robot is interacting with or is going tointeract with, or to recognize a shape, size, and/or orientation of theobject. Providing accurate object recognition for use by the robotcontrol system may increase the efficiency and/or effectiveness ofoperation of the robots.

In one example, the robot may be interacting with a stack of individualobjects, such as with a stack of boxes, as part of a de-palletizingoperation. Performing object recognition in such a scenario may bechallenging, because it may be difficult to detect the boundariesbetween individual objects and where the corners of each object begin.The object recognition may be augmented or otherwise improved throughthe recognition and identification of one or more contours, surfaces,edges, and/or corners of individual objects. More particularly, theobject recognition may be enhanced through identifying convex corners ofobject structure (i.e., of a structure of one or more objects). Forinstance, the object recognition may rely on only the convex corners,rather than on all points identified in the spatial structure data.Using the convex corners by themselves for the object recognition mayprovide a sufficient level of accuracy and may reduce the amount of timeor processing power needed to perform the object recognition.

In an embodiment, identifying the convex corners may be performed on alayer-by-layer basis. For instance, a stack of objects may have anobject structure that forms a plurality of layers. Each layer mayrepresent, e.g., a particular surface of the object structure (e.g., asurface that is parallel to ground), and may have a different height ordepth relative to other layers of the object structure. In such aninstance, a set of convex corners may be determined for each layer ofthe object structure. In an embodiment, convex corners may be identifiedfrom among, or more generally based on, vertices of a particular layerof the object structure. The vertices may be points that describe acontour of the layer, and thus may also be referred to as contourpoints.

In some implementations, identifying the convex corners may involvedetermining which vertices in spatial structure data are 3D corners. The3D corners may be 3D vertices that satisfy an orthogonality criterion,wherein the 3D vertices may generally refer to a vertex in the spatialstructure data that has a low likelihood of being an artifact introducedinto the spatial structure data as a result of noise, interference, orother source of error. For instance, the spatial structure data mayinclude, e.g., a point cloud that identifies or otherwise describes(e.g., via coordinates) a plurality of points which are locations on oneor more surfaces of an object structure. Some of the points identifiedin the point cloud may be artifacts that do not correspond to anyphysical point in the object structure. In other words, some pointsidentified in the point cloud may appear as respective vertices of thestructure, but those vertices which appear in the spatial structure datamay be artifacts that do not represent any actual physical vertex on theobject structure. Thus, one aspect of determining whether a vertex is a3D vertex or is a 3D corner herein relates to determining whether aparticular vertex identified from the spatial structure data representsa physical vertex on the object structure, or whether the identifiedvertex is an artifact.

In an embodiment, the determination of whether a vertex identified fromthe spatial structure data is an artifact may be based on whether thevertex satisfies a length criterion or multiple length criteria. Thelength criterion may be used to evaluate, e.g., whether a distancebetween a particular vertex in the spatial structure data and itsneighboring vertex meets or exceeds a defined length threshold (alsoreferred to as a threshold length). The length criterion may reflectsome situations in which a features (e.g., an edge of an objectstructure) that appears as a result of an artifact in spatial structuredata is likely to be small in size relative to other actual physicalfeatures of the object structure data because, e.g., the imaging noiseor other source of error which caused the artifact may affect only alocalized portion of the spatial structure data. Thus, a vertex thatresults from or is part of the artifact may likely be located close to aneighboring vertex or some other neighboring feature. In such anexample, a vertex which fails to satisfy the length criterion may beconsidered likely to be an artifact and may be ignored or excluded frombeing used to identify convex corners. A vertex which satisfies thelength criterion or length criteria may be eligible to be used toidentify convex corners.

In an embodiment, an orthogonality criterion may be evaluated for a 3Dvertex (or any other vertex) to determine whether the 3D vertex can be a3D corner. More specifically, the vertex may be an intersection of twoedges of the object structure. In this embodiment, the 3D corner mayinclude those 3D vertices in which the two edges are orthogonal orsubstantially orthogonal to each other (also referred to as beingsubstantially perpendicular to each other). At least some of the convexcorners may be selected or otherwise identified from among the 3Dcorners. In an embodiment, the orthogonality criterion may alsocontribute to detecting and excluding vertices that may be an artifact.In an embodiment, the orthogonality criterion may simplify objectrecognition for situations in which most or all the objects to berecognized (e.g., boxes) are expected to have orthogonal corners.

In an embodiment, identifying the convex corners may involve determininga convexity of a 3D corner. In some cases, the convexity of the vertexmay be determined based on a cross product between two vectors thatpoint away from the vertex, and/or towards two respective neighboringvertices. The cross product may be or may include a cross product vectorthat is orthogonal to two vectors. In this embodiment, the convexity ofthe vertex may be determined based on whether the cross product vectorpoints in or along a defined direction. In some cases, the plurality ofvertices may be evaluated over multiple iterations, in a sequence thatprogresses through the vertices in a clockwise manner or acounterclockwise manner along the contour of the layer. In such cases,the defined direction against which the cross product vector is comparedmay be based on whether the multiple iterations are progressing throughthe vertices in a clockwise manner or whether the multiple iterationsare progressing through the vertices in a counterclockwise manner.

In an embodiment, if a vertex does not satisfy an orthogonalitycriterion, a fused corner may be generated. The fused corner may be anorthogonal corner that is near the vertex and is generated based on thevertex. For instance, the vertex may be at an intersection point of afirst edge and a second edge that are not substantially orthogonal.Generating the fused corner may involve identifying a third edge that isorthogonal to the first edge (or to the second edge). In some cases, theedges may correspond to vectors, or to lines extending along thevectors, as discussed below in more detail. If the fused corner isconvex, it may be identified as a convex corner.

In some instances, the object recognition may involve determininggenerating or modifying a detection hypothesis based on the convexcorners. In some instances, the object recognition may involve filteringa detection hypothesis based on the convex corners. The detectionhypothesis may relate to, e.g., attempting to match spatial structuredata to a template, such as by mapping template features of the templateto the spatial structure data. The template may describe objectstructure for an object or type of object, and the template features mayidentify, e.g., a shape of the object structure, its corners or edges,or other features of the object structure. The convex corners may, e.g.,simplify the process of mapping the template features to the spatialstructure data, and/or improve an accuracy of that mapping. Forinstance, the object recognition may compare the template the featuresto only the convex corners, rather than to all of the points identifiedin the spatial structure data.

FIG. 1A illustrates a system 100 for generating and processing spatialstructure data (spatial structure data is discussed below in moredetail). The system 100 may include a computing system 101 and a spatialstructure sensing device 151. In an embodiment, the spatial structuresensing device 151 may be configured to generate spatial structure dataand may be configured to make the spatial structure data available tothe computing system 101, which may be configured to process the spatialstructure data. In some instances, the computing system 101 and thespatial structure sensing device 151 may be located in the samepremises, such as a warehouse or factory. In some instances, thecomputing system 101 and the spatial structure sensing device 151 may beremote from each other.

In an embodiment, the spatial structure sensing device 151 may beconfigured to make the spatial structure data available via acommunication interface and/or a data storage device (which may also bereferred to as a storage device). For instance, FIG. 1B depicts a system100A that is an embodiment of the system 100 of FIG. 1A. The system 100Aincludes the computing system 101, the spatial structure sensing device151, and further includes a data storage device 198 (or any other typeof a non-transitory computer-readable medium). The data storage device198 may be part of the spatial structure sensing device 151 or may beseparate from the spatial structure sensing device 151. For instance,the storage device 198 may be located in a data center that is remotefrom the spatial structure sensing device 151 and may receive and storespatial structure data generated by the spatial structure sensing device151. In this embodiment, the computing system 101 may be configured toaccess the spatial structure data by retrieving (or, more generally,receiving) the data from the data storage device 198.

In FIG. 1B, the storage device 198 may include any type ofnon-transitory computer-readable medium (or media), which may also bereferred to as a non-transitory computer readable storage device. Suchnon-transitory computer-readable medium or storage device may beconfigured to store and provide access to data. Examples of thenon-transitory computer readable medium or storage device may include,but is not limited to, an electronic storage device, a magnetic storagedevice, an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination thereof, forexample, such as a computer diskette, a hard disk, a random accessmemory (RAM), a read-only memory (ROM), an erasable programmableread-only memory (EPROM or Flash memory), a solid state drive, a staticrandom access memory (SRAM), a portable compact disc read-only memory(CD-ROM), a digital versatile disk (DVD), and/or a memory stick.

In an embodiment, the computing system 101 and the spatial structuresensing device 151 may be configured to communicate spatial structuredata via a network. For instance, FIG. 1C depicts a system 100B that isan embodiment of system 100 of FIG. 1A and/or of the system 100A in FIG.1B. In system 100B, the computing system 101 may be configured toreceive spatial structure data from the spatial structure sensing device151 via a network 199. The network 199 may provide an individual networkconnection or a series of network connections to permit the computingsystem 101 to receive spatial structure data consistent with theembodiments hereof.

In FIG. 1C, the network 199 may be connected via wired or wirelesslinks. Wired links may include Digital Subscriber Line (DSL), coaxialcable lines, or optical fiber lines. Wireless links may includeBluetooth®, Bluetooth Low Energy (BLE), ANT/ANT+, ZigBee, Z-Wave,Thread, Wi-Fi®, Worldwide Interoperability for Microwave Access(WiMAX®), mobile WiMAX®, WiMAX®-Advanced, NFC, SigFox, LoRa, RandomPhase Multiple Access (RPMA), Weightless-N/P/W, an infrared channel or asatellite band. The wireless links may also include any cellular networkstandards to communicate among mobile devices, including standards thatqualify as 2G, 3G, 4G, or 5G. Wireless standards may use various channelaccess methods, e.g., FDMA, TDMA, CDMA, or SDMA. In some embodiments,different types of data may be transmitted via different links andstandards. In other embodiments, the same types of data may betransmitted via different links and standards. Network communicationsmay be conducted via any suitable protocol, including, e.g., http,tcp/ip, udp, ethernet, ATM, etc.

The network 199 may be any type and/or form of network. The geographicalscope of the network may vary widely and the network 199 can be a bodyarea network (BAN), a personal area network (PAN), a local-area network(LAN), e.g., Intranet, a metropolitan area network (MAN), a wide areanetwork (WAN), or the Internet. The topology of the network 199 may beof any form and may include, e.g., any of the following: point-to-point,bus, star, ring, mesh, or tree. The network 199 may be of any suchnetwork topology as known to those ordinarily skilled in the art capableof supporting the operations described herein. The network 199 mayutilize different techniques and layers or stacks of protocols,including, e.g., the Ethernet protocol, the internet protocol suite(TCP/IP), the ATM (Asynchronous Transfer Mode) technique, the SONET(Synchronous Optical Networking) protocol, or the SDH (SynchronousDigital Hierarchy) protocol. The TCP/IP internet protocol suite mayinclude application layer, transport layer, internet layer (including,e.g., IPv4 and IPv4), or the link layer. The network 199 may be a typeof broadcast network, a telecommunications network, a data communicationnetwork, or a computer network.

In an embodiment, the computing system 101 and the spatial structuresensing device 151 may be able to communicate via a direct connectionrather than a network connection. For instance, the computing system 101in such an embodiment may be configured to receive the spatial structuredata via a dedicated communication interface, such as a RS-232interface, a universal serial bus (USB) interface, and/or via a localcomputer bus, such as a peripheral component interconnect (PCI) bus.

FIG. 1D illustrates a system 100C, which may be an embodiment of system100, for generating and processing spatial structure data. The system100C includes a computing system 101A, a spatial structure sensingdevice 151A, the storage device 198, and the network 199. The spatialstructure sensing device 151A is configured to capture or otherwisegenerate spatial structure data that describes a structure of one ormore objects 190. The computing system 101A is configured to access andprocess spatial structure data. In the embodiment of FIG. 1D, thecomputing system 101A may be a desktop computer, which is an embodimentof the computing system 101 of FIG. 1A, and the spatial structuresensing device 151A may be a depth-sensing camera (e.g., atime-of-flight camera or structured light camera), which is anembodiment of the spatial structure sensing device 151 of FIG. 1A.Further in this example, the computing system 101A may access thespatial structure data via any suitable means. For example, thecomputing system 101A may retrieve (or, more generally, receive) thespatial structure data from the spatial structure sensing device 151 viathe storage device 198, over the network 199, and/or via a directconnection to the spatial structure sensing device 151A.

In an embodiment, as stated above, the spatial structure data may begenerated to facilitate the control of a robot. For instance, FIG. 1Eillustrates a robot operation system 100D (which is an embodiment ofsystem 100) that is able to generate and process spatial structure data,and to control a robot 161 based on the processing. For instance, thespatial structure sensing device 151 may be a depth-sensing camera thatis configured to generate spatial structure data that describes astructure of one or more objects in a field of view of the depth-sensingcamera. The computing system 101 may be configured to receive thespatial structure data and use the data to determine a size, shape,location, and/or orientation of the one or more objects. In theseinstances, movement of the robot 161 may be controlled to interact withthe one or more objects based on their determined size, shape, location,and/or orientation.

In an embodiment, the computing system 101 may be configured to directlycontrol the movement of the robot 161 based on information determinedfrom processing the spatial structure data. For example, the computingsystem 101 may be configured to generate one or more movement commands(e.g., motor commands) based on the determined information, andcommunicate the one or more movement commands to the robot 161. In suchan example, the computing system 101 may act as a robot control system(also referred to as a robot controller).

In another embodiment, the computing system 101 may be configured tocommunicate the determined information to a robot control system that isseparate from the computing system 101, and the robot control system maybe configured to control movement of the robot 161 (e.g., by generatingone or more movement commands) based on the determined information. Forinstance, FIG. 1F depicts a robot operation system 100E (which is anembodiment of the system 100 of FIG. 1A and the system 100D of FIG. 1E)that includes a robot control system 162. More specifically, thecomputing system 101 and the spatial structure sensing device 151 inFIG. 1F may form a vision system 150 that is configured to provide tothe robot control system 162 information about an environment of therobot 161, and more specifically about objects in that environment thatthe robot 161 is to manipulate, or to interact with in some othermanner. The computing system 101 may function as a vision controllerthat is configured to process spatial structure data to determine thatinformation, which may include, e.g., a classification that indicates atype of the object, a shape or size of the objects, and/or a location ofthe objects relative to the robot 161 (e.g., relative to a robot arm ofthe robot 161). The computing system 101 may be configured tocommunicate the determined information to the robot control system 162,which may be configured to generate one or more movement commands basedon the information received from the computing system 101.

As stated above, the spatial structure sensing device 151 of FIGS. 1Athrough 1F may be configured to generate spatial structure data whichdescribes a structure of one or more objects in an environment of thespatial structure sensing device 151. As used herein, spatial structuredata refers to any type of data (also referred to as information) thatdescribes a structure of one or more physical objects (also referred toas one or more objects), and more specifically may include data aboutthe shape, orientation, arrangement, and/or size of the one or morephysical objects. In an embodiment, the spatial structure data mayinclude location data that describes a location of the structurerelative to the spatial structure sensing device 151, relative to therobot 161, or relative to some other element.

In an embodiment, the spatial structure data may comprise image data,and any and all systems, methods, and techniques described herein withrespect to spatial structure data, unless explicitly stated otherwise,may be applied equally to the image data, which is a form of the spatialstructure data. For instance, the spatial structure data may comprise animage that is or includes a depth map. The depth map may be an imagehaving a plurality of pixels and that further includes depthinformation. The depth information may include, e.g., respective depthvalues assigned to or included with some or all of the pixels. The depthvalue for a particular pixel may indicate depth of a locationrepresented by or otherwise corresponding to that pixel.

More specifically, the depth information represents informationindicative of distances along an axis that is orthogonal to an imaginaryplane on which the spatial structure sensing device 151 is located. Insome cases, if the spatial structure sensing device 151 is a camerahaving an image sensor, the imaginary plane may be an image planedefined by the image sensor. In an embodiment, depth information, asused herein may be indicative of a distance away from the spatialstructure sensing device 151. In an embodiment, depth information may bemanipulated to represent relative distances from any suitable planeparallel to the imaginary plane on which the spatial structure sensingdevice 151 is located. For instance, the suitable plane may be definedby a ceiling, floor, or wall of a room, or a platform on which one ormore objects are located. In an example, if the spatial structuresensing device 151 is located above one or more objects, depthinformation may be representative of a height of various points andsurfaces of the one or more objects relative to a surface on which theone or more objects are disposed. In another example, if one or moreobjects are displaced or otherwise offset horizontally from the spatialstructure sensing device 151, depth information may be indicative of howfar horizontally the one or more objects extend away from the spatialstructure sensing device. In an embodiment, the depth information of thespatial structure data may be indicative of and may be organizedaccording to a plurality of depth layers of the one or more objects, asdiscussed below in more detail. The plurality of depth layers includesmultiple layers, each indicative of a discrete level of depth measuredalong an axis orthogonal to the imaginary plane at which the spatialstructure sensing device 151 is located. In some embodiments, each layermay represent a single depth value. In some embodiments, each layer mayrepresent a range of depth values. Thus, although depth information mayinclude continuously variable distance measurements, a finite number oflayers may be used to capture all of the depth information.

In an embodiment, the spatial structure data may be a point cloud. Asused herein, a point cloud may identify a plurality of points thatdescribe object structure (i.e., describe a structure of one or moreobjects). The plurality of points may be, e.g., respective locations onone or more surfaces of the object structure. In some cases, the pointcloud may include a plurality of coordinates that identify or otherwisedescribe the plurality of points. For instance, the point cloud mayinclude a series of Cartesian or polar coordinates (or other datavalues) that specify respective locations or other features of theobject structure. The respective coordinates may be expressed withrespect to a coordinate system of the spatial structure sensing device151, or with respect to some other coordinate system. In some cases, therespective coordinates are discrete and spaced apart from each other butmay be understood to be representative of a contiguous surface of theobject structure. In an embodiment, the point cloud may be generatedfrom a depth map or other image data (e.g., by the computing system101).

In some embodiments, the spatial structure data may further be storedaccording to any appropriate format, such as polygon or triangular meshmodels, non-uniform rational basis spline models, CAD models,parameterization of primitives (e.g., a rectangle may be definedaccording to a center and extensions in the x, y, and z directions, acylinder can be defined by a center, a height, an upper radius, and alower radius, etc.), etc.

As stated above, the spatial structure data is captured or otherwisegenerated via the spatial structure sensing device 151. In anembodiment, the spatial structure sensing devices may be or include acamera or any other image sensing device. The camera may be adepth-sensing camera, such as a time-of-flight (TOF) camera or astructured light camera. The camera may include an image sensor, such asa charge coupled devices (CCDs) sensor and/or complementary metal oxidesemiconductors (CMOS) sensor. In an embodiment, the spatial structuresensing device may include lasers, a LIDAR device, an infrared device, alight/dark sensor, a motion sensor, a microwave detector, an ultrasonicdetector, a RADAR detector, or any other device configured to capturespatial structure data.

As further stated above, the spatial structure data generated by thespatial structure sensing device 151 may be processed by the computingsystem 101. In an embodiment, the computing system 101 may include or beconfigured as a server (e.g., having one or more server blades,processors, etc.), a personal computer (e.g., a desktop computer, alaptop computer, etc.), a smartphone, a tablet computing device, and/orother any other computing system. In an embodiment, any or all of thefunctionality of the computing system 101 may be performed as part of acloud computing platform. The computing system 101 may be a singlecomputing device (e.g, a desktop computer), or may include multiplecomputing devices.

FIG. 2A provides a block diagram that illustrates an embodiment of thecomputing system 101. The computing system 101 includes a processingcircuit 110 and a non-transitory computer-readable medium (or media)120. In an embodiment, the processing circuit 110 includes one or moreprocessors, one or more processing cores, a programmable logiccontroller (“PLC”), an application specific integrated circuit (“ASIC”),a programmable gate array (“PGA”), a field programmable gate array(“FPGA”), any combination thereof, or any other processing circuit. Inan embodiment, the non-transitory computer-readable medium 120 may be astorage device, such as an electronic storage device, a magnetic storagedevice, an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination thereof, forexample, such as a computer diskette, a hard disk, a solid state drive(SSD), a random access memory (RAM), a read-only memory (ROM), anerasable programmable read-only memory (EPROM or Flash memory), a staticrandom access memory (SRAM), a portable compact disc read-only memory(CD-ROM), a digital versatile disk (DVD), a memory stick, anycombination thereof, or any other storage device. In some instances, thenon-transitory computer-readable medium may include multiple storagedevices. In certain cases, the non-transitory computer-readable medium120 is configured to store spatial structure data received from thespatial structure sensing device 151. In certain cases, thenon-transitory computer-readable medium 120 further stores computerreadable program instructions that, when executed by the processingcircuit 110, causes the processing circuit 110 to perform one or moremethodologies described here, such as the operation described withrespect to FIG. 3.

FIG. 2B depicts a computing system 101A that is an embodiment of thecomputing system 101 and includes a communication interface 130. Thecommunication interface 130 may be configured to, e.g., receive spatialstructure data from the spatial structure sensing device 151, such asvia the storage device 198 of FIG. 1B, the network 199 of FIG. 1C, orvia a more direct connection. In an embodiment, the communicationinterface 130 may be configured to communicate with the robot 161 ofFIG. 1E or the robot control system 162 of FIG. 1F. The communicationinterface 130 may include, e.g., a communication circuit configured toperform communication over a wired or wireless protocol. As an example,the communication circuit may include a RS-232 port controller, a USBcontroller, an Ethernet controller, a Bluetooth® controller, a PCI buscontroller, any other communication circuit, or a combination thereof.

In an embodiment, the processing circuit 110 may be programmed by one ormore computer-readable program instructions stored on the storage device120. For example, FIG. 2C illustrates a computing system 101B, which isan embodiment of the computing system 101, in which the processingcircuit 110 is programmed by a data manager 202, a segmentation manager204, an object identification manager 206, and an object recognitionmanager 208. It will be understood that the functionality of the variousmanagers as discussed herein is representative and not limiting.

In various embodiments, the terms “software protocol,” “softwareinstructions,” “computer instructions,” “computer-readableinstructions,” and “computer-readable program instructions” are used todescribe software instructions or computer code configured to carry outvarious tasks and operations. As used herein, the term “manager” refersbroadly to a collection of software instructions or code configured tocause the processing circuit 110 to perform one or more functionaltasks. For convenience, the various managers, computer instructions, andsoftware protocols will be described as performing various operations ortasks, when, in fact, the managers, computer instructions, and softwareprotocols program hardware processors to perform the operations andtasks. Although described in various places as “software” it isunderstood that the functionality performed by the “managers,” “softwareprotocols,” and “computer instructions,” may more generally beimplemented as firmware, software, hardware, or any combination thereof.Furthermore, embodiments herein are described in terms of method steps,functional steps, and other types of occurrences. In an embodiment,these actions occur according to computer instructions or softwareprotocols executed by processing circuit 110.

In an embodiment, the data manager 202 is a software protocol operatingon the computing system 101. The data manager 202 is configured toaccess (e.g., receive, retrieve, store) spatial structure data, andperform any other suitable operation related to spatial structure databeing received or processed (e.g., analyzed) by the computing system101. For example, the data manager 202 may be configured to accessspatial structure data stored in non-transitory computer-readable medium120 or 198, or via the network 199 and/or the communication interface130 of FIG. 2B. The data manager 202 may also be configured to interactwith other devices through the network 199, with the data storage unit198, with the non-transitory computer-readable medium 120, and/or withthe spatial structure sensing device 151 to request, retrieve, access,send, store, or otherwise perform operations with the spatial structuredata.

In embodiments, the data manager 202 is further configured to provideaccess tools to a user to manage and manipulate spatial structure data.For example, the data manager 202 may be configured to generate and/orprovide access to databases, tables, file repositories, and other datastorage structures. In embodiments, the data manager 202 may providedata retention capabilities. The data manager 202 is configured toaccess storage device 120, data storage unit 198, and other memory unitsto archive, store, and/or otherwise retain spatial structure data andany other data generated during processes of computer system 101.

In an embodiment, the segmentation manager 204 is a software protocoloperating on control system 101. The segmentation manager 204 isconfigured to segment or extract portions of the spatial structure data.For instance, the segmentation manager 204 may be configured to extracta portion of the spatial structure data that represents a layer ofobject structure, as discussed below in more detail. Such a portion ofthe spatial structure data may represent or be referred to as a spatialsegment.

In an embodiment, the object identification manager 206 is a softwareprotocol operating on control system 101. The object identificationmanager 206 is configured to receive or access one or more spatialsegments generated by the segmentation manager 204 and provide furtherprocessing. For instance, the object identification manager 206 may beconfigured to identify vertices of a layer of the object structure, andto identify convex corners based on the vertices, as discussed below inmore detail.

In an embodiment, the object recognition manger 208 is a softwareprotocol operating on the computing system 101. The object recognitionmanager 208 may be configured to perform object recognition according tothe detected convex corners. For example, the object recognition manager208 may employ the convex corners to generate, modify, and/or filterdetection hypotheses, as discussed below in more detail.

FIG. 3 is a flow chart that illustrates example operations for a method300 for processing spatial structure data. In one example, method 300may be part of a de-palletizing procedure in which a stack of objects(e.g., boxes or other packages) are unloaded. For instance, FIG. 4Aillustrates an embodiment in which spatial structure data is generatedfor a series of objects 401, 402. The objects 401, 402 may be, e.g., astack of boxes or other packages to be unloaded from the stack by arobot 161A (which may be an embodiment of the robot 161 of FIGS. 1E and1D). The spatial structure data may describe a structure of the objects401, 402 (which may be referred to as object structure of the objects401, 402). In the example of FIG. 4A, a top surface 411 of the object401 may form a first layer of the object structure, and a top surface412 of the object 402 may form a second layer of the object structure.FIG. 4B illustrates the objects 401, 402 from the vantage point of thespatial structure sensing device 151A of FIG. 4A, which may be disposeddirectly above the objects 401, 402. From this vantage point, only thetop surfaces 411 and 412 of objects 401 and 402, respectively, arevisible to the spatial structure sensing device 151A. The surfaces 411and 412 are parallel to a platform 410, and are perpendicular to a depthdimension, which may be a dimension orthogonal to the platform 410, andparallel with axis 480. As discussed below in more detail with respectto FIG. 4C, the spatial structure data generated by the spatialstructure sensing device 151A may indicate that the top surfaces 411 and412 are at different depths relative to the spatial structure sensingdevice 151A (as measured along the axis 480 of FIG. 4A) and at differentheights relative to the platform 410.

In an embodiment, the method 300 of FIG. 3 may be performed by thecomputing system 101 of FIGS. 2A through 2C, and more specifically bythe processing circuit 110, when spatial structure data describingobject structure is stored in the non-transitory computer-readablemedium (e.g., 120) of the computing system 101. In an embodiment, thenon-transitory computer-readable medium 120 of FIGS. 2A through 2C mayfurther store a plurality of instructions (e.g., computer programinstructions) that, when executed by the processing circuit 110, causesthe processing circuit 110 to execute the method 300. In an embodiment,the method 300 may involve identifying convex corners from spatialstructure data and using the convex corners for a subsequent (orcontemporaneous) object recognition operation.

In an embodiment, method 300 of FIG. 3 includes an operation 302, inwhich the processing circuit 110 of the computing system 101 (of FIGS.2A-2C) accesses spatial structure data, which describes objectstructure. In some cases, operation 302 may be performed by the datamanger 202 of FIG. 2C. In an embodiment, accessing the spatial structuredata may involve retrieving (or, more generally, receiving) the spatialstructure data from the non-transitory computer-readable medium 120 orfrom any other device. In some situations, the spatial structure datamay have been received by the computing system 101 from the spatialstructure sensing device 151, such as via the communication interface130 of FIG. 2B, and may have been stored in the non-transitorycomputer-readable medium 120, which may provide a temporary buffer orlong-term storage for the spatial structure data. For instance, thespatial structure data may include a point cloud received from thespatial structure sensing device 151 and stored in the non-transitorycomputer-readable medium 120. The point cloud may then be accessed in bythe processing circuit 110 in operation 302.

In some situations, the spatial structure data that is accessed may bestored in the non-transitory computer-readable medium 120 and may havebeen generated beforehand by the processing circuit 110 itself based oninformation received from the spatial structure sensing device 151. Forinstance, the processing circuit 110 may be configured to generate apoint cloud based on raw sensor data received from the spatial structuresensing device 151 and may be configured to store the generated pointcloud in the non-transitory computer-readable medium 120. The pointcloud may then be accessed by the processing circuit 110 in operation302 (e.g., by retrieving the data from the non-transitorycomputer-readable medium 120).

As stated above, the spatial structure data may describe a structure ofone or more objects, such as the objects 401, 402 in FIG. 4A. In anembodiment, the spatial structure data may include a depth map, whichmay have a plurality of pixels [u, v], some or all of which may have adepth value. Each of the pixels in the depth map may correspond to arespective physical location captured or otherwise represented by thepixel, and the depth value may indicate a distance from the spatialstructure sensing device (e.g., 151A of FIG. 4A) and that physicallocation. The physical location may be on, e.g., a surface of theobjects 401, 402, or a surface of the platform 410 on which the objects401, 402 are located. In some cases, the distance may be measured alongthe axis 480 that is orthogonal to an imaginary plane 430 at which thespatial structure sensing device 151A is located. In some cases, thedepth value assigned to a pixel [u, v] may represent a Z-component ofcoordinate of the corresponding physical location. For instance, thespatial structure sensing device 151A may be a depth-sensing camera. Insuch an instance, a particular pixel [u, v] in the depth map maycorrespond to a physical location on a surface of the objects 401, 402or of the platform 410, wherein the physical location has a coordinate[X, Y, Z] whose X-component and Y-component are based on an inverseprojection matrix of the depth-sensing camera, and whose Z-component isequal to or otherwise based on the depth value assigned to the pixel [uv] (the coordinate [X Y Z] may be in a coordinate system of thedepth-sensing camera).

In an embodiment, the spatial structure data may include a point cloud.As stated above, the point cloud may include a plurality of coordinatesthat identify a plurality of points that are physical locations onobject structure, such as physical locations on one or more surfaces ofthe objects 401, 402 of FIG. 4A. In an embodiment, if the spatialstructure data includes the point cloud, it may in some scenarios begenerated (e.g., by the spatial structure sensing device 151 orcomputing system 101) based on the depth map discussed above.

FIG. 4C illustrates an example of a point cloud that represents objectstructure for the objects 401, 402 of FIG. 4A. More specifically, thefigure illustrates a plurality of points represented by the point cloud.The plurality of points may be physical locations on one or moresurfaces of the objects 401, 402. The point cloud may include arespective coordinate (e.g., [X Y Z] coordinate) for each of theplurality of points. The density of the physical points that arerepresented by the point cloud may be greater or less than what isillustrated in FIG. 4C, depending on a resolution of the spatialstructure sensing device 151/151A. The Z-component of the coordinate ofa particular point may represent a depth value of that point. In theexample of FIG. 4C, the point cloud may identify a depth value of Z=Z₁(e.g., 150 cm) for physical points on the top surface 411 of FIG. 4A ofthe object 401, identify a depth value of Z=Z₂ (e.g., 200 cm) forphysical points on the surface 412 of FIG. 4A of the object 402, andidentify a depth value of Z=Z₃ (e.g., 260 cm) for physical points on thesurface of the platform 410 of FIG. 4A.

In an embodiment, the spatial structure data may have depth informationindicative of a plurality of layers of a structure of one or moreobjects (e.g., 401, 402). In some cases, each layer may indicate orinclude points (e.g., physical locations) on the structure that have thesame depth value or substantially the same depth value, or indicatepoints on the structure that change in depth value by a gradual amountand/or in a smooth, continuous manner (as opposed to a sharp or abruptmanner). For instance, FIG. 4C relates to a point cloud that identifiesa first set of points 411 a that represents the top surface 411 of theobject 401, identifies a second set of points 412 a that represents thetop surface 412 of the object 402, and identifies a third set of points410 a that represents the top surface of the platform 410. In thisexample, the first set of points 411 a have the same depth value of Z₁,and thus may represents a first layer of the object structure.Similarly, the second set of points 412 a may all have the same depthvalue of Z₂, while the third set of points 410 a may all have the samedepth value of Z₃, and thus the second set of points 412 a and the thirdset of points 410 a may represent a second layer and a third layer,respectively, of the object structure for the one or more objects 402and the platform 410. As stated above, each layer may correspond to adifferent surface of the object structure. For instance, the first layermay correspond to the top surface 411 of FIG. 4A, and the second layermay correspond to the top surface 412. In some cases, the layers maycorrespond to only surfaces that are parallel to an image plane of thespatial structure sensing device 130, such as the imaginary plane 430.

In an embodiment, points that are represented by a point cloud (or otherform of spatial structure data) may be divided into different layersbased on a sharp change in depth value. A change may be considered sharpif, e.g., it has an absolute value or a rate of change that exceeds adefined threshold. For instance, the points represented by the pointcloud for FIG. 4C includes a change of Z₂−Z₁ (e.g., 50 cm) between thefirst set points 411 a and the second set of points 412 a, and includesa change of Z₃−Z₂ (e.g., 60 cm) between the second set of points 412 aand the third set of points 410 a, and more specifically betweenneighboring points. Such changes may exceed a defined threshold (e.g.,0.5 cm or 1 cm), and thus may be considered a sharp change. Because ofthe sharp change, the first set of points 411 a, second set of points412 a, and the third set of points 410 a may be considered to be ondifferent layers of the object structure.

Referring back to FIG. 3, the method 300 may further include anoperation 304 in which the processing circuit 101 of the computingsystem 101 extracts, from the spatial structure data, a portion of thespatial structure data representative of one layer of the plurality oflayers (the portion may also be referred to as a spatial segment). In anembodiment, operation 304 may be performed by the segmentation manager204. If the spatial structure data captures an image of the one or moreobjects, such as a grayscale image or color image, the portion that isextracted from the spatial structure data may also be referred to as animage segment. In some implementations, the portion that is extractedmay represent only one layer of the plurality of layers of the objectstructure.

FIG. 4D depicts an example in which operation 304 extracts, from thepoint cloud or other spatial structure data, a first portion thatrepresents a first layer 421 of the object structure and extracts asecond portion that represents a second layer 422 of the objectstructure. The first portion may include the first set of points 411 a,or more specifically a first set of coordinates representing thosepoints. The second portion may include the second of points 412 a, andmore specifically a second set of coordinates that represent the secondset of points 412 a. FIG. 4E also illustrates the first layer 421 andthe second layer 422 that are extracted, and further illustrates acontour 423 of the first layer 421 and a contour 424 of the second layer422. Thus, in an embodiment, the spatial structure data comprises apoint cloud that identifies a plurality of points which are respectivelocations on one or more surfaces of the object structure, and theportion of the spatial structure data that is extracted identifies a setof points which are representative of a particular layer.

In an embodiment, operation 304 may involve extracting, from the spatialstructure data, data values (e.g., coordinates) that identify a set ofpoints that have the same depth value, or whose depth values are withina defined range. For instance, operation 304 for the example of FIGS. 4Cand 4D may involve identifying the first set of points 411 a from thespatial structure data in that example, because the first set of points411 a have the same depth value. Similarly, operation 304 mayalternatively or additionally involve identifying the second set ofpoints 412 a from the spatial structure data because the second set ofpoints 412 a have the same depth value.

In an embodiment, the portion that is extracted in operation 304 (whichmay be referred to as a spatial segment) may represent a portion of thespatial structure data that has depth values which fall within a definedrange corresponding to one layer of the plurality of layers. Whenoperation 304 is complete, each spatial segment may represent adifferent layer of the plurality of layers. In an embodiment, all orsome of the total plurality of layers may be represented by the spatialsegments. In an embodiment, some layers of a structure of the one ormore objects may have no corresponding surfaces at the appropriate depthand therefore have no corresponding spatial segments. The number ofspatial segments extracted in operation 304 (e.g., by the segmentationmanager 204) in further examples will correspond to the number ofdetected layers represented by the spatial structure data.

In an embodiment, operation 304 may involve the computing system 101dividing the spatial structure data into portions or segments thatrepresent different levels of the object structure, or that representssurfaces of different respective depths for that object structure. Insome cases, dividing the spatial structure data may be based onidentifying respective portions of the spatial structure data that havedifferent depth values. In some cases, dividing the spatial structuredata may be based on detecting a sharp change in depth value amongportions of the spatial structure data. For instance, performingoperation 304 for FIG. 4D may involve detecting a sharp change in depthvalue between two sets of points of the spatial structure data of thisexample, from a depth value of Z₁ to a value of Z₂. The two sets may beidentified as the first set of points 411 a and the second set of points412 a.

In an embodiment, operation 304 may accommodate one or more objects thathave a structure with an angled surface. The angled surface may be,e.g., a surface which is not parallel with the spatial structure sensingdevice 151/151A, and more specifically is not parallel with an imageplane thereof, e.g., imaginary plane 430 of FIG. 4A. The angled surfacemay thus have different depth values, relative to the spatial structuresensing device 151/151A, across the surface. In embodiments, layers maybe selected to accommodate such angled surfaces. For instance, a layermay be selected for spatial segmentation that includes the entirety ofthe angled surface. Thus, the layer in the spatial structure data may beselected specifically to ensure that the angled surface is contained bya single spatial segment.

As stated above, the portion of the spatial structure data beingextracted in operation 304 may also be referred to as a spatial segment.In an embodiment, the spatial segments being extracted may be stored(e.g., by the segmentation manager 204) as masks. Each mask may includeinformation specifying one or more regions that is part of a respectivelayer of the object structure of the one or more objects and may excludeall regions that is not part of the respective layer of the objectstructure. In other words, the mask may include information specifyingone or more regions that is part of the structure at a given depth andmay exclude all regions that is not part of the structure at the givendepth. Each spatial segment may be stored in, e.g., the data storagedevice 198 of FIG. 1B or other non-transitory computer-readable storagemedium. In an embodiment, the spatial segment may be received forfurther processing by the object identification manager 206 of FIG. 2C,either directly from the segmentation manager 204 or via the datastorage device 198 or non-transitory computer-readable medium 120 ofFIG. 2A.

Returning to FIG. 3, the method 300 may include an operation 306, inwhich the processing circuit 110 of the computing system 101 identifies,from the portion of the spatial structure data extracted in operation304, a plurality of vertices (also referred to as contour points) thatdescribe a contour of the layer discussed with respect to operation 304.For instance, FIG. 5A illustrates a plurality of vertices 512 a-512 fthat are extracted for the layer 422. In an embodiment, operation 306may be performed by the object identification manager 506.

As discussed above, in some cases the spatial structure data comprises apoint cloud that identifies points on a surface of an object structure.The portion extracted from the point cloud that is representative of thelayer in operation 304 may identify a set of points (e.g., points 412 aof FIG. 4D) that are extracted from the plurality of points identifiedby the point cloud. In such cases, the processing circuit 110 mayperform operation 306 by, e.g., identifying a plurality of line segmentsthat form edges (e.g., straight edges) of the set of points representingthe layer (e.g., 422), and identifying the plurality of vertices ofoperation 306 as endpoints of the line segments. For instance, FIG. 5Bdepicts a plurality of line segments 513 a-513 f that form straightedges of the set of points 412 a. Each of the line segments may be aline that has two opposite ends defined by two respective endpoints. Forinstance, line segment 513 a has two opposite ends defined by endpoint512 a and 512 b, which may be identified as two vertices in operation306. In some cases, a particular line segment may be considered an edgeif it extends through edge points of the set of points 412 a, asillustrated in FIG. 5B. The edge points of a set of points (e.g., of set412 a) may be those points that are on a periphery of the set of points.More generally, the edge points may be points that represent one or moreouter edges of a particular layer. In some cases, a particular linesegment may be near the edge points and may be considered an edge evenif some points of the set of points extend slightly past that linesegment. In some cases, a particular line segment may be considered anedge only if no points of the set of points extend past that linesegment.

In some cases, the processing circuit 110 may perform operation 306 byextracting (or, more generally, identifying) a plurality of edge pointsfrom among the set of points (e.g., 412 a), and determining a pluralityof lines that fit through the plurality of edge points. In such cases,the processing circuit 110 may identify, as the plurality of vertices,intersection points at which the plurality of lines intersect. Forinstance, FIG. 5C illustrates a plurality of lines 514 a-514 e that fitthrough a plurality of edge points of the portion of spatial structuredata extracted from operation 304. As an example, the line 514 f may bea line that is fitted so as to approximate a contour of the edge points515 in FIG. 5C. The figure illustrates only a portion of the edge pointsof the layer 422, which surround an entire contour of the layer. FIG. 5Cfurther depicts the plurality of vertices 512 a-512 f as points ofintersection between the lines 514 a-514 e. For instance, the vertex 512a may be an intersection point between the line 514 a and the line 515 fThe plurality of vertices identified in operation 306 (e.g., 512 a-512f) may include all of the points of intersections between the linesdiscussed above (e.g., lines 514 a-514 f), or only a subset of thepoints of intersection.

In an embodiment, the edge points 515 represented by the capturedspatial structure data may not line up in a straight line. In such anembodiment, operation 306 may involve (e.g., via object identificationmanager 206) fitting the set of lines 514 a-514 f in a manner that bestapproximates respective locations of the edge points. The process offitting the set of lines may use any suitable algorithm to fit lines 514a-514 f to edge points 515, including, for example, least squaresanalysis and others. After generating the lines 514 a-514 f, theplurality of vertices 512 a-512 f may be identified according to theintersections of the lines 514 a-514 f. Each or some of the intersectionpoints where two or more of the lines 514 a-514 f intersect may bedefined or otherwise identified as one of the vertices 512 a-512 f ofthe layer 422. Thus, the plurality of vertices 512 a-512 f may define acontour of the layer. In an embodiment, the plurality of vertices whichare identified using the technique illustrated in FIG. 5B may be thesame as the plurality of vertices which are identified using thetechnique illustrated in FIG. 5C.

Returning to FIG. 3, method 300 may include an operation 308, in whichthe processing circuit 110 of the computing system 110 identifies convexcorners of the layer based on the plurality of vertices. In anembodiment, operation 308 may be performed by the object identificationmanager 206. The convex corners may be or represent, e.g., physicallocations on the object structure from which it is more convenient, moreefficient, or more effective to perform object recognition, or physicallocations at which interaction between the one or more objects and therobot (e.g., 161 of FIG. 1F) may be convenient. In some scenarios, if anobject structure includes a box or other rectangular prism, the convexcorners may include, e.g., some of or all outside corners of therectangular prism, and in some scenarios, the convex corners may includeor represent only those locations. In some scenarios, a convex corner ofan object structure may be an exterior corner for which a plane can bedrawn that intersects the object structure at only that corner. Asdiscussed below in more detail, some embodiments of identifying theconvex corners may involve identifying 3D corners that are convex. The3D corners may be 3D vertices that satisfy an orthogonality criterion,as discussed below in more detail. The 3D vertices may be vertices thatsatisfy a first length criterion and/or a second length criterion, asalso discussed below in more detail. The first length criterion, thesecond length criterion, and the orthogonality criterion may moregenerally be referred to as a first criterion, a second criterion, and athird criterion, respectively. In some embodiments, identifying theconvex corners may involve determining or otherwise identifying one ormore fusion corners, which are also discussed below in more detail.

In an embodiment, operation 308 may involve determining a relationshipthat is indicative of at least a distance or distances between two ormore vertices from among the plurality of vertices (such as thoseidentified from operation 306). The operation may further involveidentifying a subset of the plurality of vertices as 3D cornersaccording to the relationship, and identifying, as the convex corners,3D corners that are convex. In some cases, the relationship is furtherindicative of respective angles formed by pairs of vectors, wherein eachof the vectors extend between a pair of vertices of the plurality ofvertices.

In an embodiment, the 3D corners may be 3D vertices that satisfy anorthogonality criterion, as discussed below in more detail. Operation308 may involve identifying a set of 3D vertices based on therelationship between the vertices. A 3D vertex may generally be a vertexthat has a low likelihood of being an artifact. More specifically,noise, interference, or other sources of error may introduce an artifactin the spatial structure data accessed in operation 302. A vertex thatis an artifact of the spatial structure data refers to a portion of thespatial structure data that does not represent a physical vertex on theobject structure. Such a vertex may be excluded from the set of 3Dvertices. In some cases, the set of 3D corners are identified from amongthe set of 3D vertices, and at least some of the convex corners may beidentified from among the 3D corners, as discussed below in more detail.

The set of 3D vertices may, in some implementations, include verticesthat each has a respective distance to a neighboring vertex which isequal to or exceeds a defined length threshold (also referred to as athreshold length). The set of 3D vertices may further exclude any vertexthat has a respective distance to a neighboring vertex which is lessthan the defined length threshold. In other words, because the spatialstructure data is based on sensor data and is therefore subject tonoise, error, artifacts, and other imperfections, the plurality ofidentified vertices may not represent the corners or other physicalfeatures of the structure of the one or more objects with completeaccuracy. For example, an object may have a structure that is arectangular prism (also referred to as a rectangular box) with fourvertices on one layer of that structure, but the spatial structure datafor the structure of that object may indicate that the layer has sevenvertices. Thus, an operation may be performed to distinguish 3D verticesof the spatial structure data, which are vertices that have a lowlikelihood of being an artifact, from vertices that are likely to beartifacts in the spatial structure data.

In some cases, this operation is performed based on one or more lengthcriteria. The one or more length criteria may evaluate whether adistance from a particular vertex to a neighboring vertex (e.g., anearest neighboring vertex) exceeds a defined threshold. For instance,if two vertices are too close together (e.g., based on the thresholdlength and/or length criteria) in the spatial structure data, one of thevertices may be an artifact, because noise or interference which causethe artifact may be localized within the spatial structure data, andthus any feature which is an artifact or that appears as a result of theartifact may be small in size relative to actual physical features ofthe object structure. Thus, as discussed below in more detail, one ormore length criteria may be used to identify which vertices should beincluded as a 3D vertex in a set of 3D vertices. Further, the set of 3Dcorners may include 3D vertices, from among the set of 3D vertices, thatrepresent an orthogonal corner of the object structure.

In an embodiment, operation 308 involves determining (e.g., by theobject identification manager 206) whether each vertex of the pluralityof vertices (of operation 306) is a 3D corner, based on the lengthcriteria and/or orthogonality criterion discussed above. Such adetermination may further involve determining, for each vertex of theplurality of vertices, a relationship between that vertex and at leastone other vertex from the plurality of vertices, or relationship betweenthat vertex and two other vertices (e.g., two nearest neighboringvertices).

In an embodiment, identifying 3D corners as part of operation 308 mayinvolve determining whether to include a first vertex of the pluralityof vertices into a set of 3D corners. As an example, the first vertexmay be vertex 512 b in FIG. 5D. In some cases, operation 308 may involvemultiple iterations that evaluate all of the plurality of vertices todetermine whether they are 3D vertices and/or to determine whether theyare 3D corners, and the first vertex may be, e.g., part of one of theiterations. The iterations may, e.g., progress through the plurality ofvertices in a sequence that follows the contour 423 of the layer 422 ina clockwise manner or a counterclockwise manner. In such cases, thefirst vertex may be a current vertex or a current contour point (whichmay be referred to as cVx or cPt) for that particular iteration.

The above determination of whether the first vertex is a 3D corner mayinvolve selecting (e.g., by the object identification manager 206), fromamong the plurality of vertices, a second vertex that is a nearestneighboring vertex to the first vertex in a first direction along thecontour of the layer. For instance, the second vertex may be vertex 512a, which is a nearest neighboring vertex (also referred to as a closestneighboring vertex) to the vertex 512 b in a first direction A (e.g., acounterclockwise direction as illustrated by the dashed arrow) along thecontour 423 of the layer 422. As stated above, operation 308 may involvemultiple iterations that progress through the plurality of verticesidentified in operation 306 (e.g., in a clockwise manner) so as toevaluate each vertex of the plurality of vertices to determine whetherthe vertex is a 3D vertex and/or to determine whether the vertex is a 3Dcorner. In this example, the second vertex may be a previous vertex orprevious contour point (which is referred to as pVx or pPt). Theprevious vertex may be a vertex that was evaluated in a previousiteration (e.g., a previous consecutive iteration). For instance, if acurrent iteration is evaluating vertex 512 b to determine whether it isa 3D corner, then vertex 512 b may be the current vertex, and vertex 512a may be a previous vertex. Further, vertex 512 c in this example may bea next vertex. The next vertex or next contour point may be a vertexthat will be evaluated in the next consecutive iteration to determinewhether that vertex is a 3D vertex and/or to determine whether thatvertex is a 3D corner (which may be referred to as nVx or nPt).

The above embodiment of determining whether the first vertex is a 3Dcorner may further involve defining a first vector that is from thefirst vertex to the second vertex (and thus points away from the firstvertex). For example, the first vector may be the vector 551 in FIG. 5D.The first vector 551 of FIG. 5D may, in some cases, be a version of theline segment 513 a of FIG. 5B, in which the line segment hasdirectionality. In some instances, if the first vertex is represented bya first coordinate [X₁, Y₁, Z₁], and the second vertex is represented bya second coordinate [X₂, Y₂, Z₂], the first vector {right arrow over(v₁)} may be defined as <X₂−X₁, Y₂−Y₁, Z₂−Z₁>. In some cases, theZ-component may be ignored if the vertices have the same Z-component fortheir respective coordinates. In such cases, the first vector {rightarrow over (v₁)} may be defined as <X₂−X₁, Y₂−Y₁>. In some cases, thefirst vector is from a current vertex or current contour point (cVx orcPt) to a previous vertex or previous contour point (pVx or pPt).

The above embodiment of determining whether a first vertex is a 3Dcorner may further involve selecting, from among the plurality ofvertices, a third vertex that is a closest neighboring vertex to thefirst vertex in a second direction along the contour of the layer,wherein the second direction is different from the first directiondiscussed above. For instance, the third vertex may be vertex 512 c inFIG. 5D, which is a closest neighboring vertex to the vertex 512 b in asecond direction B (e.g., clockwise direction as illustrated by thedashed arrow) along the contour 423 of the layer defined by theportion/spatial segment. The second direction B is different than thefirst direction A. More specifically, the first direction A of FIG. 5Dmay be a direction of flow that traces the contour 423 of the layer inthe figure in a counterclockwise manner, while the second direction Bmay be a direction of flow that traces the contour 423 in a clockwisemanner. In some cases, the third vertex may be the next vertex or nextcontour point (referred to as nVx or nPt). Further, a second vector maybe determined in this embodiment, wherein the second vector is from thefirst vertex to the third vertex (and thus also points away from thefirst vertex). For instance, the second vector may be vector 552 in FIG.5D, which is from the vertex 512 b to the vertex 512 c. Similar to theabove example, if the third vertex is represented by a third coordinate[X₃, Y₃, Z₃], the second vector {right arrow over (v₂)} may be definedas <X₃−X₁, Y₃−Y₁, Z₃−Z₁>. In some cases, the second vector may be fromthe current vertex or current contour point to the next vertex or nextcontour point.

The above embodiment may further involve determining whether the firstvector {right arrow over (v₁)} satisfies a first length criterion, andwhether the second vector {right arrow over (v₂)} satisfies a secondlength criterion. Such a determination may involve comparing a length ofthe first vector (which may be referred to as a first length) to adefined threshold length and comparing a length of the second vector(which may be referred to as a second length) to the defined lengththreshold. The first length may be, e.g., a distance from the vertex 512b to the vertex 512 a. In some instances, if the first vector is avector from a current vertex cVx to a previous vertex pVx, the firstlength may be defined as Norm(cVx−pVx), wherein “Norm” is a functionthat determines a Euclidean distance between two points. The secondlength may be, e.g., a distance from the vertex 512 b to the vertex 512c. In some instances, if the second vector is a vector from a currentvertex cVx to a next vertex nVx, the second distance may be defined asNorm(cVx−nVx). As stated above, comparing the vectors' lengths to athreshold length ensures that the first vertex (e.g., 512 b) is farenough away from the second vertex (e.g., 512 a) and the third vertex(e.g., 512 c) to determine that the first vertex has a low likelihood ofbeing an artifact. In some cases, the vertex may be considered to be a3D vertex if it satisfies the first length criterion and the secondlength criterion. In some cases, a vertex is determined not to be a 3Dvertex (and thus not a 3D corner) if it fails to satisfy either of thefirst and second length criteria, or if it fails to satisfy both thefirst and second length criteria. If the vertex is determined not to bea 3D vertex, it may be excluded from being used to identify convexcorners. For instance, the processing circuit 110 may ignore the vertexwhen identifying convex corners, which is discussed below in moredetail. In some cases, the processing circuit 110 may be configured toremove, from the spatial structure data, data values corresponding tosuch a vertex which is not a 3D vertex.

The threshold length in the above example may be determined or otherwisedefined according to one or more of several techniques. In embodiments,the threshold length may be predetermined by a user, manager, or otheroperator of the computing system 101 of FIGS. 1A-1C. The thresholdlength may be predetermined according to knowledge of the objects ortype of objects that are being sensed or will be sensed by the spatialstructure sensing device 151 of FIGS. 1A-1C. For example, if the objectsare known to have edges with a minimum expected length, the thresholdlength may be selected to eliminate or otherwise filter out vertices ofthe spatial structure data that form edges which are less than theminimum expected length. In some cases, the threshold length may bedetermined based on knowledge of the spatial structure sensing device151 (e.g., knowledge of a level of accuracy for a type of spatialstructure sensing device to which device 151 belongs).

In embodiments, the minimum expected length may be multiplied by acorrection factor to arrive at the threshold length. The correctionfactor may be, e.g., a predetermined scalar value between 0 and 1. Thecorrection factor may be determined according to an amount of noise inthe spatial structure data. If the spatial structure data is noisy, thecorrection factor may be a smaller number. With noisy spatial structuredata, it is expected that there will be a greater number of vertices inthe spatial structure data that are artifacts. A smaller correctionfactor lowers the threshold length to account for greater variation andnoise in the spatial structure data, so as to filter out more artifacts.If, on the other hand, the spatial structure data is less noisy, thecorrection factor may be a larger number (i.e., raising the thresholdlength where there is less variation or noise in the spatial structuredata). In embodiments, spatial structure data may be analyzed for noiseby the object identification manager 206, which may then select acorrection factor according to a measure of the noise in the spatialstructure data. In embodiments, the correction factor may be selected orotherwise determined according to an expected arrangement of objects.This feature is illustrated with reference to FIG. 5E, which depicts alayer 590 defined by a portion/spatial segment of spatial structure datawhich describes a structure of objects 580, 581. The objects 580, 581may be of the same size, and may be arranged irregularly or offset fromone another. For instance, such objects 580, 581 are not aligned witheach other. Such objects 580, 581 may be sensed as having a structurewith edges of different lengths, due to offsets in the arrangement. Morespecifically, the objects 580 and 581 are square shaped and are the samesize, each with four edges of identical lengths. Accordingly, a minimumexpected length may be selected as the length of any one edge, e.g.,edge 583 of the object 580. However, FIG. 5E depicts a spatial offsetbetween object 580 and 581, which creates a short edge 584 in thespatial segment 590. If the correction factor is set as a high value(e.g., close to 1), the threshold length will be only slightly shorterthan the length of edge 583. Such a high value for the correction factorwill thus result in edge 584 failing to meet the first length criterionand second length criterion discussed above, which may cause, e.g.,vertex 582 to not be recognized as a 3D vertex, even though vertex 582is not an artifact. Accordingly, in embodiments, the objectidentification manager 206 may select a smaller correction factor (e.g.,a correction factor with a value that is closer to 0), resulting in athreshold length smaller than the length of the edge 584, such that thevertex 582 does not fail the first and second length criteria.

In an embodiment, the above embodiment of determining whether the firstvertex is a 3D corner may further involve determining whether the firstvertex satisfies the orthogonality criterion. This may involvedetermining whether the first vector {right arrow over (v₁)} and thesecond vector discussed above are substantially orthogonal to eachother. For instance, the orthogonality criterion may involve evaluatingwhether the vector 551 of FIG. 5D and the vector 552 of FIG. 5D aresubstantially orthogonal (also referred to as being substantiallyperpendicular). As used herein, substantially orthogonal refers to twolines or two vectors that meet at an angle that is approximately 90°.Approximately 90° may include a range around 90° of +/−0.1°, 0.5°, 1°,2°, 3°, 4°, and/or 5° degrees. In some cases, the above determinationmay be performed by determining whether a dot product of the firstvector {right arrow over (v₁)} and the second vector {right arrow over(v₂)} is zero or substantially zero. In an embodiment, a vertex (e.g.,vertex 512 b) may be determined to a 3D corner, or to be included in aset of 3D corners, if the vertex satisfies the first length criterion,the second length criterion, and the orthogonality criterion discussedabove. In an embodiment, each vertex of the plurality of verticesidentified in operation 306 may be evaluated to determine whether thevertex is a 3D corner. If a vertex is a 3D vertex that satisfies thefirst and second length criteria but is not a 3D corner because it doesnot satisfy the orthogonality criterion, the 3D vertex may still be usedto determine a fused corner, as discussed below in more detail.

In some embodiments, determining whether the first vector {right arrowover (v₁)} and the second vector {right arrow over (v₂)} (e.g., vector551 and vector 552) are substantially orthogonal to each other mayinvolve projecting (e.g., by the object identification manager 206) thetwo vectors to a shared plane, and then determining whether theprojected vectors are substantially orthogonal. For instance, aportion/spatial segment extracted from the spatial structure data mayinclude spatial information with a range of depth values and mayrepresent a layer that forms an angled surface (e.g., relative to animage plane of the spatial structure sensing device 151 of FIGS. 1A-1C).In such situations, the first {right arrow over (v₁)} and the secondvector {right arrow over (v₂)} may be out of plane with each other. Toaccount for such situations, the object identification manager 206 (or,more generally, the processing circuit 110 of the computing system 101)may be configured to project the first vector {right arrow over (v₁)}and the second vector {right arrow over (v₂)} onto a shared plane and touse the angle between the projected vectors to determine whether theyare substantially orthogonal to each other.

In an embodiment, operation 308 further involves determining a convexityof a vertex that is a 3D corner (i.e., determining a convexity of the 3Dcorner), or more generally whether the 3D corner is a convex corner.This determination may be performed by, e.g., the object identificationmanager 206. A convex corner of a shape may be a corner where an angleinterior to the shape is less than 180°, while a concave corner a shapemay be a corner where an angle exterior to the shape is less than 180°.For instance, vertex 512 b in FIG. 5D may be a 3D corner because itsatisfies the orthogonality criterion and may further be a convex cornerbecause an angle 516 interior to the shape of FIG. 5D at the vertex 512b has an angle of substantially 90°, which is less than 180°. The vertex512 d in FIG. 5D may be a 3D corner because it also satisfies theorthogonality criterion but may be a concave corner because an angle 517exterior to the shape of FIG. 5D at vertex 512 d is substantially 90°,which is less than 180°. For a three-dimensional object, a convex cornerof a shape can be understood as a corner for which a plane can be drawnthat intersects the shape at only one point—the convex corner itself.Convex corners and concave corners may also be defined by the content ofthe space around them. For instance, a vertex may be understood as apoint at which four quadrants intersect and determining a convexity ofthe vertex may involve determining how many quadrants contain a portionof the shape. If only one quadrant contains part of the shape, thevertex may be determined to be concave. If three quadrants contain partof the shape, the vertex may be determined to be convex.

In an embodiment, determining whether a vertex that is a 3D corner isalso a convex corner involves determining (e.g., by the objectidentification manager 206) a cross product of the first {right arrowover (v₁)} and the second vector {right arrow over (v₂)}, which are thetwo vectors discussed above that point away from the vertex. The crossproduct may include or may be a cross product vector. For instance, FIG.5F illustrates cross product vectors 553 and 556 that are a result ofcross product operations. More specifically, the figure provides aperspective view of the layer 422. FIG. 5F also illustrates the locationof the spatial structure sensing device 151A with respect to thecaptured spatial segment 422. In the example of FIG. 5F, the crossproduct vector 553 is a result of a cross product of vectors 551, 552,and is orthogonal to both vectors 551, 552. Further, the cross productvector 556 is a result of a cross product of vectors 554, 555. Thedirection of cross product vector 553 or 556, as determined by the righthand rule, may indicate whether a particular vertex that is a 3D corner(e.g., vertex 512 b or vertex 512 d in FIG. 5F) is also a convex corneror a concave corner. The right hand rule is a commonly used conventionfor determining the direction of a cross product vector. For example,consider the cross product equation {right arrow over (a)}×{right arrowover (b)}={right arrow over (c)}. Using the right hand, the index fingeris pointed in the direction of {right arrow over (a)} and the middlefinger is pointed in the direction of {right arrow over (b)}. When theindex finger and middle finger are arranged thusly, the extended thumbpoints in the direction of the cross product, {right arrow over (c)}.Determining whether a vertex is a convex corner may involve determiningwhether a direction of a cross product vector corresponding to thevertex matches a defined direction. As discussed below in more detail,the direction against which the cross product vector is matched may be adirection in which the computing system 101 is progressing through theplurality of vertices (of operation 306).

Because the cross product operation is anti-commutative, the ordering ofthe vectors and vertices influences the result of the above-describeddetermination. For instance, if the processing circuit 110 of thecomputing system 101 is determining whether each vertex of the pluralityof vertices is a 3D vertex, determining whether each vertex is a 3Dcorner, and/or determining whether each vertex is a convex corner, theordering of the vertices may refer to whether the processing circuit 110is performing the above determination in a sequence that progressesthrough the plurality of vertices in a clockwise manner or acounterclockwise manner. As an example, if the processing circuit 110evaluates the vertices 512 a, 512 b, 512 c, 512 d, 512 e, and 512 f ofFIG. 5D or 5F in that order to determine whether each vertex is a convexcorner, the processing circuit 110 may be considered to be progressingthrough the vertices in a clockwise manner along a contour 423 of thelayer 422. In such an example, the defined direction against which across product vector is compared may be a direction that is pointing outof the layer 422 depicted in FIG. 5F (also referred to as pointingupwards from the layer towards the spatial structure sensing device151A). In this example, a vertex may be considered a convex corner ifits cross product vector is pointing in or otherwise matches the defineddirection and may be considered a concave corner if its cross productvector is not pointing in, or more generally does not match, the defineddirection. For instance, if the vertices of FIG. 5F are evaluated, thecross product vector 553, which is a cross product of vectors 551 and552, will point out of the layer 422 (or upwards from the layer)represented by the spatial segment. The cross product vector 533 may beconsidered to match the defined direction, and thus its correspondingvertex (512 b) may be considered a convex corner. In the example of FIG.5F, the first vector 551 points along the direction A, which may beconsidered a counterclockwise direction or considered a direction thatfollows a counterclockwise flow along the contour 423.

In an embodiment, the sequence that progresses through the plurality ofvertices may be analogized to a flow that follows a contour (e.g.,contour 423) that reaches the plurality of vertices of the contourconsecutively in a clockwise manner or a counterclockwise manner, andthe defined direction (against which a cross product vector is compared)may be opposite to that of a curl vector of the flow. For example, ifthe evaluation of the plurality of vertices progresses in a clockwisemanner, the curl vector may point downward (away from the spatialstructure sensing device 151A), as defined by the right hand rule. Thedefined direction may be opposite to that of the curl vector. If theplurality of vertices are evaluated in a counterclockwise manner, thecurl vector may point upward.

In the example of FIG. 5F, operation 308 may determine that vertex 512 dis a concave corner, or more generally that vertex 512 d is not a convexcorner. As stated above, vertex 512 d may be considered a concave cornerof the contour 423 because an angle exterior to the contour 423 at thevertex 512 is less than 180°. Determining whether the vertex 512 d is aconcave corner or a convex corner may also be based on a cross productof two vectors pointing away from the vertex toward two respectivenearest neighboring vertices. For FIG. 5F, these two vectors are vectors554, 555. Vector 554 points towards a nearest neighboring vertex in adirection that follows a counterclockwise progression, while vector 555points toward a nearest neighboring vertex in a direction that follows aclockwise progression. The cross product of vectors 554, 556 includes across product vector 556. The cross product vector 556 points into thelayer (also referred to as pointing downward, away from the spatialstructure sensing device 151A). A direction of the cross product vector556 (downward) may be opposite to the defined direction (upward). Thus,the vertex 512 d corresponding to the cross product vector 556 may beconsidered to be a concave corner, or more generally not to be a convexcorner.

As discussed above, the ordering of the vectors and vertices used incomputing the cross product may affect a direction of the resultingcross product vector. In the example shown of FIG. 5F, the vertices canbe evaluated over multiple respective iterations, in a sequence thatprogresses through the vertices in a clockwise manner (also referred toas a clockwise sequence), wherein a first vector {right arrow over (v₁)}used to calculate the cross product points in a direction that follows acounterclockwise flow (from a current vertex towards a previous vertex),and a second vector {right arrow over (v₂)} used to calculate the crossproduct points in a direction that follows a clockwise flow (from thecurrent vertex towards a next vertex). The cross-product vector of{right arrow over (v₁)}×{right arrow over (v₂)} may point in a directiontoward the spatial structure sensing device 151A. In another example,the vertices may be evaluated in a sequence that progresses through thevertices in a counterclockwise manner (also referred to as acounterclockwise sequence). In such an example, the first vector {rightarrow over (v₁)} used to calculate the cross product points in adirection that follows a clockwise flow (from a current vertex towards aprevious vertex), and a second vector {right arrow over (v₂)} used tocalculate the cross product points in a direction that follows acounterclockwise flow (from the current vertex towards a next vertex).The cross-product vector of {right arrow over (v₁)}×{right arrow over(v₂)} may point in a direction away the spatial structure sensing device151A. Thus, the cross product vectors in the two above examples maypoint in different directions, depending on whether the vertices areprogressed through in a clockwise manner or in a counterclockwisemanner. Thus, when identifying whether a vertex is a convex corner, theprocessing circuit 110 (e.g., executing the object identificationmanager 206) may take into account whether the vertices are beingevaluated in a clockwise manner or a counterclockwise manner. Forexample, if the vertices are being evaluated in a clockwise manner, theprocessing circuit 110 may determine whether the defined direction(against which cross product vectors are compared) as a direction thatpoints upward, toward the spatial structure sensing device 151A. If thevertices are being evaluated in a counterclockwise manner, theprocessing circuit 110 may determine whether the defined direction as adirection that points downward, away from the spatial structure sensingdevice 151A.

In embodiments, the processing circuit 110 (e.g., while executing objectidentification manager 206) may perform the determination of whichvertices are 3D corners and the determination of which vertices areconvex corners in any suitable order. In some embodiments, thedetermination of which vertices are 3D corners may have to be performedbefore determining which vertices are convex corners. The processingcircuit 110 may evaluate all of the plurality of vertices of operation306 to identify 3D corners before determining which vertices are alsoconvex corners, or the processing circuit may begin identifying convexcorners after only some vertices have been evaluated to determinewhether those vertices are 3D corners. In some embodiments, theprocessing circuit may determine whether a particular vertex is a convexcorner right after the vertex is determined as a 3D corner.

In an embodiment, operation 308 may involve determining a fused corner,which may be considered a convex corner. In some cases, the fused cornermay be an orthogonal corner of a shape that approximates the objectstructure described by the spatial structure data, and that is near thevertex being evaluated. The fused corner may be determined as part of acorner fusion technique, which is discussed below in more detail. Suchan embodiment may apply to circumstances in which a vertex is determinedas a 3D vertex (because it satisfies the first length criterion and thesecond length criterion) but is not a 3D corner (because it does notsatisfy the orthogonality criterion). For instance, FIG. 5G depicts alayer 622 of a structure that is described by a portion/spatial segmentof spatial structure data, wherein the layer is extracted in, e.g.,operation 304. More specifically, the figure depicts a plurality ofvertices 532 a-532 g that describe a contour of the layer 622. Thevertices may have been identified in, e.g., operation 306. In thisexample, vertex 532 b may be a first vertex (e.g., current vertex) ofthe plurality of vertices while the vertex 532 a may be a second vertex(e.g., a previous vertex), and the vertex 532 c may be a third vertex(e.g., a next vertex). The vertex 532 a may be a nearest (also referredto as closest) vertex to the vertex 532 b in a first direction A alongthe contour of the layer 622, while the vertex 532 c is a nearest vertexto the vertex 532 b in a second direction B along the contour of thelayer 622. A first vector 561 may be defined to extend from the vertex532 b to the vertex 532 a, while a second vector 562 may be defined toextend from the vertex 532 b to the vertex 532 c. In this example,vertex 532 b may be determined to be a 3D vertex because it satisfiesthe first length criterion and the second length criterion but is notconsidered to be a 3D corner because it does not satisfy theorthogonality criterion. The orthogonality criterion is not satisfiedbecause the first vector 561 and the second vector 562 are notorthogonal to each other.

In such a situation, the processing circuit 110 may select a fourthvertex that is a second closest neighboring vertex to the first vertexin the second direction along the contour of the layer and may define athird vector between the fourth vertex and the third vertex. In theexample of FIG. 5G, the fourth vertex is 532 d, which is a secondclosest neighboring vertex to the vertex 532 b along the direction B.Further, the third vector in this example is vector 563, which isbetween vertex 532 c and vertex 532 d, and more specifically is fromvertex 532 c to vertex 532 d. In some cases, the processing circuit 110may (e.g., by executing the object identification manager 206) determinewhether the third vector satisfies a third length criterion by comparingthe third vector 563 to the threshold length. In some cases, such acomparison may be omitted.

In the above example of the corner fusion technique, the processingcircuit 110 may further determine or otherwise define a first line thatextends along the first vector, and a second line that extends along thethird vector. For example, FIG. 5G depicts an example in which the firstline is line 571, and the second line is line 573. Line 571 may extendalong the vector 561 and may extend past the vertex 532 a and extendpast the vertex 532 b. Similarly, line 573 extends along the vector 563,and extends past the vertex 532 c and 532 d.

In the above example of the corner fusion technique, the processingcircuit 110 may further identify an intersection point (e.g., point 532h of FIG. 5G) that is an intersection between the first line (e.g., 571)and the second line (e.g., 573). Upon identifying point 532 h as avertex, point 532 h may be added to the plurality of vertices along withvertices 532 a-g. As depicted in FIG. 5G, the intersection point 532 hmay be an imaginary point that is outside a shape formed by the layerdepicted in the figure, or more specifically outside of a contour of thelayer. In some cases, the processing circuit 110 may further determinewhether the intersection point (e.g., 532 h) satisfies the orthogonalitycriterion, such as by determining whether the first line (e.g., 571) isorthogonal or substantially orthogonal to the second line (e.g., 573).If the first line and the second line that define the intersection pointare orthogonal to each other, the intersection point may be referred toas a fused corner.

In the above example of the corner fusion technique, the processingcircuit 110 may further determine whether the fused corner is convex. Insome cases, this determination may be based on determining a crossproduct of the first vector and the third vector, similar to thediscussion above of a cross product between the first vector and thesecond vector. The convexity of the fused corner may be based on whethera direction of a corresponding cross product vector matches the defineddirection discussed above. In some cases, determining whether the fusedcorner is convex may involve determining whether the fused corner isoutside of the contour of the layer being described by the plurality ofvertices. For instance, point 532 h is a fused corner that is convex,because it is outside of the contour 623 of the layer 622. If the fusedcorner is determined to be convex, it may be identified as a convexcorner of operation 308.

As stated above, determining whether a vertex is a 3D corner, ordetermining a fused corner based on the vertex, may be performed foreach of a plurality of vertices, through a plurality of iterations. Inthe example discussed above with respect to FIG. 5G, the currentiteration is being performed for vertex 532 b, such that vertex 532 b isthe current vertex, and vertices 532 a and 532 c are the previous vertexand the next vertex, respectively, for that iteration. In the nextconsecutive iteration, the processing circuit 110 may progress to thenext unchecked vertex. After identifying point 532 h as a fused corner,the processing circuit 110 may skip vertex 532 c, and proceed todetermining whether vertex 532 d is 3D vertex or 3D corner. Becausevertex 532 c was used in determining point 532 h as a fused corner, itis not necessary to assess this point again.

In further embodiments, after identifying point 532 h as a fused corner,the processing circuit may progress to a next nearest neighboring vertexto the point 532 h. For instance, in this example, it may progress tovertex 532 c. In this example, the vertex 532 c becomes the currentvertex, while point 532 h, which has now been added to the plurality ofvertices, and 532 d are the previous vertex and next vertex,respectively, for that iteration. Because point 532 h is on line 573,which connects vertices 532 c and 532 d, vertex 532 c cannot satisfy thethird criterion (orthogonality), and the processing circuit 110 willdetermine that vertex 532 c is not a 3D vertex or 3D corner.

The corner fusion method, as described above, may be advantageous inmultiple situations. Such situations may involve irregularly shapedobject having non-perpendicular (i.e., angled or rounded) corners,and/or may involve a particular physical object having a corner that isobscured or obstructed in view relative to the spatial structure sensingdevice 151/151A of FIGS. 1A-1D and FIG. 5F. For example, a first objectmay have a corner that is obscured or obstructed in view by a secondobject that is stacked on top of the first object. In another example, adifference in coloring or material (e.g., reflective tape) at a cornerof an object may result in an inaccurate spatial structure data capture.In such situations, the corner fusion technique may compensate for suchinaccuracies.

Returning to FIG. 3, the method 300 further includes an operation 310,in which the processing circuit 110 of the computing system employs 101performs object recognition according to the convex corners identifiedin operation 308. In some instances, operation 310 may be performed bythe object recognition manager 208. In an embodiment, operation 310 mayinclude generating a detection hypothesis, modifying a detectionhypothesis, and/or filtering a detection hypothesis, as describedherein.

In an embodiment, a detection hypothesis may refer to an estimate ofwhat object, type of object, and/or object orientation is being sensedby the spatial structure sensing device 151 of FIGS. 1A-1C. In someinstances, the detection hypothesis may be a determination of whether aparticular object or type of object (e.g., a particular type of box orother packaging) is being described by spatial structure data, such asthe spatial structure data of operation 302. In some cases, thedetection hypothesis may be a determined mapping between the spatialstructure data and template features of a template, wherein the templatedescribes an object structure. The template may describe the objectstructure through template features, such as a series of coordinates forcorners, surfaces, and/or edges of the object structure. FIG. 6A depictsan example of a template for a particular type of object, such as aparticular type of box that is part of the inventory at a warehouse. Asstated above, the template may describe a plurality of features of thetype of object, such as a series of coordinates that represent corners,edges, one or more surfaces, or some other features. In the example ofFIG. 6A, the template includes four coordinates that describe fourpoints 612 a-612 d on a particular type of object (e.g., a box). Thefour coordinates may represent, e.g., four corners of that type ofobject. In some implementations, a detection hypothesis may include anestimate, for one or more of the features in the template discussedabove, of which one or more portions of the spatial structure data mapsor otherwise correspond to the one or more features.

In an embodiment, operation 310 involves using the convex cornersidentified in operation 308 to generate a detection hypothesis. Forexample, the convex corner represented by point 512 a in FIG. 6A may beused to generate a detection hypothesis. The detection hypothesis inFIG. 6A may be an estimated mapping, which maps point 612 a of thetemplate to the convex corner represented by point 512 a, as illustratedin FIGS. 6A and 6B. In some cases, the detection hypothesis may alsoinvolve a determination that the points 612 a-612 d of the template areto be aligned with the identified convex corners. For instance, such adetermination may involve the points 612 a-612 d having an orientationsuch that a line between points 612 a and 612 d is parallel with a linebetween the points 512 a and 512 f, and/or such that a line betweenpoints 612 a and 612 b is parallel with a line between points 512 a and512 b. Using the convex corners of step 308 to generate a detectionhypothesis may increase a chance that the detection hypothesis iscorrect. More specifically, the convex corners that are identified inoperation 308 may more likely correspond to physical features of astructure of one or more objects (e.g., 401, 402 of FIG. 4A), such ascorners of the one or more objects. If the template also identifiesphysical features of a particular object or type of object, then adetection hypothesis in which a feature of the template is matched to aconvex corner may have a higher likelihood of being accurate. In somecases, the detection hypothesis may be modified and refined, and theconvex corners may provide a starting point for determining a correctdetection hypothesis. Thus, in an embodiment, operation 310 may involvedetermining whether the convex corners align with features in a templatethat defines an object shape.

In an embodiment, operation 310 may involve determining whether tofilter out or otherwise ignore a detection hypothesis. More generallyspeaking, such an embodiment may involve determining whether thedetection hypothesis is likely to be incorrect. For instance, FIG. 6Cdepicts another detection hypothesis, which involves another estimate ofwhich portion of the template (represented by points 612 a-612 d) aremapped to another portion of the spatial structure data. In thisembodiment, because none of the template features are mapped to a convexcorner (e.g., point 512 a), the detection hypothesis may be filtered orotherwise ignored for purposes of object recognition. In anotherexample, a detection hypothesis may have to map at least one templatefeature of a template to a portion of the spatial structure data thatrepresents a location which is sufficiently close to a convex corner. Ifthe detection hypothesis does not map any template feature to a locationthat is sufficiently close to a convex corner, the detection hypothesismay be filtered out.

Further embodiments consistent with the disclosure include at least thefollowing.

Embodiment 1 is a computing system, comprising a non-transitorycomputer-readable medium; at least one processing circuit configured,when spatial structure data describing object structure is stored in thenon-transitory computer-readable medium, to: access the spatialstructure data, the spatial structure data having depth informationindicative of a plurality of layers for the object structure; extract,from the spatial structure data, a portion of the spatial structure datarepresentative of one layer of the plurality of layers; identify, fromthe portion of the spatial structure data, a plurality of vertices thatdescribe a contour of the layer; identify convex corners of the layerbased on the plurality of vertices; and perform object recognitionaccording to the convex corners.

Embodiment 2 is the computing system of embodiment 1, wherein thespatial structure data includes a point cloud that identifies aplurality of points which represent respective locations on one or moresurfaces of the object structure, and wherein the portion of the spatialstructure data that is extracted identifies a set of points whichrepresent a portion of the plurality of points and which arerepresentative of the layer.

Embodiment 3 is the computing system of embodiment 2, wherein theprocessing circuit is configured to identify the plurality of verticesthat describe the contour of the layer by: identifying a plurality ofline segments that form straight edges for the set of pointsrepresenting the layer and identifying the plurality of vertices asendpoints of the line segments.

Embodiment 4 is the computing system of embodiment 2 or 3, wherein theprocessing circuit is configured to identify the plurality of verticesthat describe the contour of the layer by: identifying a plurality ofedge points from among the set of points, wherein the edge pointsrepresent points that are on a periphery of the set of points;determining a plurality of lines that fit through the plurality of edgepoints; and identifying, as the plurality of vertices, intersectionpoints at which the plurality of lines intersect.

Embodiment 5 is the computing system of any of embodiments 1-4, whereinthe processing circuit is further configured to identify the convexcorners of the layer from among the plurality of vertices by:determining a relationship that is indicative of at least a distance ordistances between two or more vertices from among the plurality ofvertices; identifying a subset of the plurality of vertices as 3Dcorners according to the relationship; and identifying, as the convexcorners, 3D corners that are convex.

Embodiment 6 is the computing system of embodiment 5, wherein therelationship is further indicative of respective angles formed by pairsof vectors, each of the vectors extending between a pair of vertices ofthe plurality of vertices.

Embodiment 7 is the computing system of any of embodiments 1 to 6,wherein the processing circuit is further configured to identify theconvex corners of the layer from among the plurality of vertices by:identifying a set of 3D vertices from among the plurality of vertices,identifying a set of 3D corners from among the set of 3D vertices, andidentifying at least some of the convex corners from among the set of 3Dcorners, wherein the set of 3D vertices include vertices that each has arespective distance to a nearest neighboring vertex which is equal to orexceeds a defined threshold length, and excludes any vertex that has arespective distance to a nearest neighboring vertex which is less thanthe defined threshold length, and wherein the set of 3D corners include3D vertices, from among the set of 3D vertices, that represent anorthogonal corner of the object structure.

Embodiment 8 is the computing system of embodiment 7, wherein theprocessing circuit is further configured to identify the set of 3Dcorners by determining whether to include a first vertex of theplurality of vertices into the set of 3D corners, by: selecting, fromamong the plurality of vertices, a second vertex that is a nearestneighboring vertex to the first vertex in a first direction along thecontour of the layer; defining a first vector that is from the firstvertex to the second vertex; selecting, from among the plurality ofvertices, a third vertex that is a nearest neighboring vertex to thefirst vertex in a second direction along the contour of the layer, thesecond direction being different from the first direction; defining asecond vector that is from the first vertex to the third vertex;determining whether the first vertex satisfies a first length criterionby comparing a first length of the first vector to the defined thresholdlength; determining whether the first vertex satisfies a second lengthcriterion by comparing a second length of the second vector to thedefined threshold length; determining whether the first vertex satisfiesan orthogonality criterion by determining whether the first vector andthe second vector are substantially orthogonal to each other; and inresponse to a determination that the first vertex does not satisfy thefirst length criterion, that the first vertex does not satisfy thesecond length criterion, or that the first vertex does not satisfy theorthogonality criterion, determining to exclude the first vertex fromthe set of 3D corners, in response to a determination that the firstvertex satisfies the first length criterion, that the first vertexsatisfies the second length criterion, and that the first vertexsatisfies the orthogonality criterion, determining to include the firstvertex in the set of 3D corners.

Embodiment 9 is the computing system of embodiment 8, wherein theprocessing circuit is further configured, in response to a determinationto include the first vertex as a 3D corner in the set of 3D corners, tofurther determine whether the 3D corner is a convex corner bydetermining a cross product of the first vector and the second vector todetermine a convexity of the 3D corner.

Embodiment 10 is the computing system of embodiment 9, whereindetermining the cross product includes determining a cross productvector, and wherein determining whether the 3D corner is a convex cornerincludes determining whether a direction of the cross-product vectormatches a defined direction.

Embodiment 11 is the computing system of embodiment 10, wherein theprocessing circuit is configured to determine, for each vertex of theplurality of vertices, whether to include the vertex as a respective 3Dcorner in the set of 3D corners, and to perform the determination in ansequence that progresses through the plurality of vertices along thecontour of the layer in a clockwise manner or a counterclockwise manner,and wherein the defined direction against which the direction of thecross product vector is compared depends on whether the sequenceprogresses through the plurality of vertices in the clockwise manner orwhether the sequence progresses through the plurality of vertices in thecounterclockwise manner.

Embodiment 12 is the computing system of any of embodiments 8 to 11,wherein the processing circuit is configured, in response to adetermination that the first vertex does not satisfy the orthogonalitycriterion, to determine a fused corner based on the first vertex,wherein the fused corner is an orthogonal corner of a shape that isbased on the object structure, and is determined by: selecting a fourthvertex that is a second nearest neighboring vertex to the first vertexin the second direction along the contour of the layer; defining a thirdvector between the fourth vertex and the third vertex; determining afirst line that extends along the first vector, and a second line thatextends along the third vector; identifying an intersection pointbetween the first line and the third line; determining whether theintersection point satisfies the orthogonality criterion by determiningwhether the first line and the third line are substantially orthogonalto each other; and identifying the intersection point as the fusedcorner in response to a determination that the intersection pointsatisfies the orthogonality criterion.

Embodiment 13 is the computing system of embodiment 12, wherein theprocessing circuit is configured: to determine whether the fused corneris convex by determining whether the fused corner is outside of thecontour of the layer, and to identify the fused corner as one of theconvex corners in response to a determination that the fused corner isconvex.

Embodiment 14 is the computing system of any of embodiments 8 to 13,wherein the processing circuit is further configured to project thefirst vertex, the second vertex, and the third vertex onto a sharedplane prior to defining the first vector and the second vector and priorto determining whether the first vertex satisfies the first lengthcriterion, the second length criterion, and the orthogonality criterion.

Embodiment 15 is the computing system of any of embodiments 1 to 14,wherein the processing circuit is further configured to perform theobject recognition by generating a detection hypothesis according to theconvex corners.

Embodiment 16 is the computing system of any of embodiments 1 to 15,wherein the processing circuit is configured to perform objectrecognition by determining, based on the convex corners, how to map thespatial structure data, which describes the object structure, tofeatures in a template that also describes the object structure.

Embodiment 17 is the computing system of any of embodiments 1 to 16,wherein the processing circuit is further configured to perform theobject recognition by modifying a detection hypothesis according to theconvex corners.

Embodiment 18 is a non-transitory computer-readable medium havinginstructions stored thereon that, when executed by a processing circuit,causes the processing circuit to: access spatial structure data thatdescribes object structure, wherein the spatial structure data has depthinformation indicative of a plurality of layers for the objectstructure; extract, from the spatial structure data, a portion of thespatial structure data representative of one layer of the plurality oflayers; identify, from the portion of the spatial structure data, aplurality of vertices that describe a contour of the layer; identifyconvex corners of the layer based on the plurality of vertices; andperform object recognition according to the convex corners.

Embodiment 19 is the non-transitory computer-readable medium ofembodiment 18, wherein the instructions, when executed by the processingcircuit, cause the processing circuit to identify the convex corners ofthe layer from among the plurality of vertices by: determining arelationship that is indicative of at least a distance or distancesbetween two or more vertices from among the plurality of vertices;identifying a subset of the plurality of vertices as 3D cornersaccording to the relationship; and identifying, as the convex corners,3D corners that are convex.

Embodiment 20 is a method performed by a computing system, the methodcomprising: accessing spatial structure data that describes objectstructure, wherein the spatial structure data has depth informationindicative of a plurality of layers for the object structure;extracting, from the spatial structure data, a portion of the spatialstructure data representative of one layer of the plurality of layers;identifying, from the portion of the spatial structure data, a pluralityof vertices that describe a contour of the layer; and identifying convexcorners of the layer based on the plurality of vertices; and performingobject recognition according to the convex corners.

It will be apparent to one of ordinary skill in the relevant arts thatother suitable modifications and adaptations to the methods andapplications described herein can be made without departing from thescope of any of the embodiments. The embodiments described above areillustrative examples and it should not be construed that the presentinvention is limited to these particular embodiments. It should beunderstood that various embodiments disclosed herein may be combined indifferent combinations than the combinations specifically presented inthe description and accompanying drawings. It should also be understoodthat, depending on the example, certain acts or events of any of theprocesses or methods described herein may be performed in a differentsequence, may be added, merged, or left out altogether (e.g., alldescribed acts or events may not be necessary to carry out the methodsor processes). In addition, while certain features of embodiments hereofare described as being performed by a single component, module, or unitfor purposes of clarity, it should be understood that the features andfunctions described herein may be performed by any combination ofcomponents, units, or modules. Thus, various changes and modificationsmay be affected by one skilled in the art without departing from thespirit or scope of the invention as defined in the appended claims.

1-20. (canceled)
 21. A computing system, comprising: a non-transitorycomputer-readable medium; at least one processing circuit configured,when spatial structure data describing object structure is stored in thenon-transitory computer-readable medium, to: access the spatialstructure data for the object structure; identify, from the spatialstructure data, a plurality of vertices that describe a contour of theobject structure; and identify convex corners associated with the objectstructure based on the plurality of vertices.
 22. The computing systemof claim 21, wherein the at least one processing circuit is furtherconfigured, when the spatial structure data is stored in thenon-transitory computer-readable medium, to perform object recognitionaccording to the convex corners.
 23. The computing system of claim 21,wherein the at least one processing circuit is further configured, whenthe spatial structure data is stored in the non-transitorycomputer-readable medium and the spatial structure data has depthinformation indicative of a plurality of layers for the objectstructure, to extract, from the spatial structure data, a portion of thespatial structure data representative of a layer of the plurality oflayers for the object structure, wherein the contour that is identifiedis a contour of the layer.
 24. The computing system of claim 21, whereinthe spatial structure data includes a point cloud that identifies a setof points which represent respective locations on one or more surfacesof the object structure.
 25. The computing system of claim 24, whereinthe at least one processing circuit is configured to identify theplurality of vertices that describe the contour of the object structureby: identifying a plurality of line segments that form straight edgesfor the set of points, and identifying the plurality of vertices asendpoints of the line segments.
 26. The computing system of claim 24,wherein the at least one processing circuit is configured to identifythe plurality of vertices that describe the contour by: identifying aplurality of edge points from among the set of points, wherein the edgepoints represent points that are on a periphery of the set of points;determining a plurality of lines that fit through the plurality of edgepoints; and identifying, as the plurality of vertices, intersectionpoints at which the plurality of lines intersect.
 27. The computingsystem of claim 21, wherein the at least one processing circuit isfurther configured to identify the convex corners from among theplurality of vertices by: determining a relationship that is indicativeof at least a distance or distances between two or more vertices fromamong the plurality of vertices; identifying a subset of the pluralityof vertices as 3D corners according to the relationship; andidentifying, as the convex corners, 3D corners that are convex.
 28. Thecomputing system of claim 21, wherein the at least one processingcircuit is further configured to identify the convex corners from amongthe plurality of vertices by: identifying a set of 3D vertices fromamong the plurality of vertices, identifying a set of 3D corners fromamong the set of 3D vertices, and identifying at least some of theconvex corners from among the set of 3D corners, wherein the set of 3Dvertices include vertices that each has a respective distance to anearest neighboring vertex which is equal to or exceeds a definedthreshold length, and excludes any vertex that has a respective distanceto a nearest neighboring vertex which is less than the defined thresholdlength, and wherein the set of 3D corners include 3D vertices, fromamong the set of 3D vertices, that represent an orthogonal corner of theobject structure.
 29. The computing system of claim 28, wherein the atleast one processing circuit is further configured to identify the setof 3D corners by determining whether to include a first vertex of theplurality of vertices into the set of 3D corners, by: selecting, fromamong the plurality of vertices, a second vertex that is a nearestneighboring vertex to the first vertex in a first direction along thecontour; defining a first vector that is from the first vertex to thesecond vertex; selecting, from among the plurality of vertices, a thirdvertex that is a nearest neighboring vertex to the first vertex in asecond direction along the contour, the second direction being differentfrom the first direction; defining a second vector that is from thefirst vertex to the third vertex; determining whether the first vertexsatisfies a first length criterion by comparing a first length of thefirst vector to the defined threshold length; determining whether thefirst vertex satisfies a second length criterion by comparing a secondlength of the second vector to the defined threshold length; determiningwhether the first vertex satisfies an orthogonality criterion bydetermining whether the first vector and the second vector aresubstantially orthogonal to each other; and in response to adetermination that the first vertex does not satisfy the first lengthcriterion, that the first vertex does not satisfy the second lengthcriterion, or that the first vertex does not satisfy the orthogonalitycriterion, determining to exclude the first vertex from the set of 3Dcorners, in response to a determination that the first vertex satisfiesthe first length criterion, that the first vertex satisfies the secondlength criterion, and that the first vertex satisfies the orthogonalitycriterion, determining to include the first vertex in the set of 3Dcorners.
 30. The computing system of claim 29, wherein the at least oneprocessing circuit is further configured, in response to a determinationto include the first vertex as a 3D corner in the set of 3D corners, tofurther determine whether the 3D corner is a convex corner bydetermining a cross product of the first vector and the second vector todetermine a convexity of the 3D corner.
 31. The computing system ofclaim 29, wherein determining the cross product includes determining across product vector, and wherein determining whether the 3D corner is aconvex corner includes determining whether a direction of thecross-product vector matches a defined direction, wherein the at leastone processing circuit is configured to determine, for each vertex ofthe plurality of vertices, whether to include the vertex as a respective3D corner in the set of 3D corners, and to perform the determination ina sequence that progresses through the plurality of vertices along thecontour in a clockwise manner or a counterclockwise manner, and whereinthe defined direction with which the direction of the cross productvector is compared depends on whether the sequence progresses throughthe plurality of vertices in the clockwise manner or whether thesequence progresses through the plurality of vertices in thecounterclockwise manner.
 32. The computing system of claim 29, whereinthe at least one processing circuit is configured, in response to adetermination that the first vertex does not satisfy the orthogonalitycriterion, to determine a fused corner based on the first vertex,wherein the fused corner is an orthogonal corner of a shape that isbased on the object structure, and is determined by: selecting a fourthvertex that is a second nearest neighboring vertex to the first vertexin the second direction along the contour; defining a third vectorbetween the fourth vertex and the third vertex; determining a first linethat extends along the first vector, and a second line that extendsalong the third vector; identifying an intersection point between thefirst line and the third line; determining whether the intersectionpoint satisfies the orthogonality criterion by determining whether thefirst line and the third line are substantially orthogonal to eachother; and identifying the intersection point as the fused corner inresponse to a determination that the intersection point satisfies theorthogonality criterion.
 33. The computing system of claim 32, whereinthe at least one processing circuit is configured: to determine whetherthe fused corner is convex by determining whether the fused corner isoutside of the contour of the object structure, and to identify thefused corner as one of the convex corners in response to a determinationthat the fused corner is convex.
 34. The computing system of claim 29,wherein the at least one processing circuit is further configured toproject the first vertex, the second vertex, and the third vertex onto ashared plane prior to defining the first vector and the second vectorand prior to determining whether the first vertex satisfies the firstlength criterion, the second length criterion, and the orthogonalitycriterion.
 35. The computing system of claim 21, wherein the at leastone processing circuit is further configured to control robotinteraction with the object structure according to the convex corners.36. The computing system of claim 21, wherein the at least oneprocessing circuit is configured to perform object recognition bydetermining, based on the convex corners, how to map the spatialstructure data, which describes the object structure, to features in atemplate that also describes the object structure.
 37. The computingsystem of claim 21, wherein the at least one processing circuit isfurther configured to perform the object recognition by modifying adetection hypothesis according to the convex corners.
 38. Anon-transitory computer-readable medium having instructions storedthereon that, when executed by at least one processing circuit, causesthe at least one processing circuit to: access spatial structure datathat describes object structure; identify, from the spatial structuredata, a plurality of vertices that describe a contour of the objectstructure; and identify convex corners associated with the objectstructure based on the plurality of vertices.
 39. The non-transitorycomputer-readable medium of claim 38, wherein the instructions, whenexecuted by the at least one processing circuit, cause the at least oneprocessing circuit to identify the convex corners from among theplurality of vertices by: determining a relationship that is indicativeof at least a distance or distances between two or more vertices fromamong the plurality of vertices; identifying a subset of the pluralityof vertices as 3D corners according to the relationship; andidentifying, as the convex corners, 3D corners that are convex.
 40. Amethod performed by a computing system, the method comprising: accessingspatial structure data that describes object structure; identifying,from the spatial structure data, a plurality of vertices that describe acontour of the object structure; and identifying convex cornersassociated with the object structure based on the plurality of vertices.